1.
Cooperative-Based Clustering in Transportation
Network Optimization
May 8, 2014
1 Introduction
A promising approach on transportation network optimization that combines
the strengths of various clustering algorithms is introduced. The main idea of
this approach was inherited from document clustering but has been modied
for use on graph data.
1.1 Transportation Network Optimization (TNO)
A transportation network is a dynamic, stochastic, and complex system. Mod-
eled as a graph, nodes fall into categories that correspond to manufacturing
sources, distribution centers, and end customers.
The most common objective in transportation network optimization is to
nd the shortest-distance distribution on a network, i.e., to determine an opti-
mal set of routes between suppliers and customers. Today there is a growing
interest in a new and much more sophisticated class of network solutions that
involve multiple optimization factors like prot, service level, fault tolerance (or
resilience), and environmental footprint, with the optimal solution balancing
the complex trade-os among all these parameters simultaneously.
1.2 Cluster Analysis in TNO
Designing a distribution network often involves planning of routes over regions
or deciding on locations for warehouses. Cluster analysis oers an alternative
solution to categorize locations in a systematic way and speeds up the process
of exploring several dierent versions of the clusters.
Although clustering algorithms have been successfully applied in specic
transportation network optimization problems, but the question of how can we
identify the clustering algorithm best suited for a particular problem remains
unanswered. My hypothesis is that an ensemble approach that synthesizes a
solution from the results of an aggregation of constituent clustering algorithms,
with multiple optimization factors like prot, service level, fault tolerance (or
resilience), and environmental footprint, will produce measurably better results
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2.
Extracting
Program
Structure
CC/G
{Ca1 ….. Cam}
{Cb1 ….. Cbm}
{Cd1 ….. Cdm}
{Ce1 ….. Cem}
Clusters Set
CC/G
Clusters
graph
structure
inherent in
software
Software
Program1
Software
Programn
Structure
Partitioning
• Hill Climbing (with three
different configurations)
• Lattix Partitioning
Algorithm
Evaluation
• Partitioning Quality
• Similarity
Figure 1: Data ow diagram for cooperative based clustering in software archi-
tecture recovery
than any one of those algorithms individually. This approach has been explored
in various disciplines, but according to our knowledge no work has been done
for transportation network optimization.
1.3 Cooperative Based Clustering
Cooperative Clustering on Graphs is an unsupervised learning algorithm for
clustering a graph networks into k partitions based on intra-cluster density and
inter-cluster sparsity. The main idea is to apply dierent clustering algorithms
on the graph network. Each algorithm will provide dierent k clusters. A com-
mon agreement among those clusterings is then found. This agreement identies
the minimum number of k clusters that the graph network should have. The last
step is to merge the remaining graph elements that exhibited disagreement in-
between the clusters that were initially determined using optimum intra-cluster
density and inter-cluster sparsity. These steps are illustrated by gure 1. Figure
1 has been adopted from a recent published paper in the software engineering
domain [2].
Relevant Publication
1. A. Ogunbanwo, A. Williamson, M. Veluscek, R. Izsak, T. Kalganova, P.
Broomhead (2013), Transportation Network Optimization, Encyclopedia
of Business Analytics and Optimization.
2. A. Ibrahim, D. Rayside, R. Kashef, Cooperative Based Software Clus-
tering on Dependency Graphs, IEEE Canadian Conference on Electrical
and Computer Engineering (CCECE), Toronto, Canada (May, 2014).
3. R. Naseem, O. Maqbool, and S. Muhammad, Cooperative clustering for
software modularization, Journal of Systems and Software, 2013.
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3.
4. Rasha Kashef and Mohamed S. Kamel, Cooperative clustering,Pattern
Recognition, vol. 43, no. 6, pp. 2315 2329, 2010.
5. William Eberle and Lawrence Holder. Anomaly Detection in Data Rep-
resented as Graphs. Intelligent Data Analysis: An International Journal.
Volume 11, Number 6, pp. 663-689. 2007
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